Geographical patterns of social cohesion drive disparities in early COVID infection hazard.

The uneven spread of COVID-19 has resulted in disparate experiences for marginalized populations in urban centers. Using computational models, we examine the effects of local cohesion on COVID-19 spread in social contact networks for the city of San Francisco, finding that more early COVID-19 infections occur in areas with strong local cohesion. This spatially correlated process tends to affect Black and Hispanic communities more than their non-Hispanic White counterparts. Local social cohesion thus acts as a potential source of hidden risk for COVID-19 infection.

Spatial heterogeneity can lead to substantial local variations in COVID-19 timing and severity.

Standard epidemiological models for COVID-19 employ variants of compartment (SIR or susceptible-infectious-recovered) models at local scales, implicitly assuming spatially uniform local mixing. Here, we examine the effect of employing more geographically detailed diffusion models based on known spatial features of interpersonal networks, most particularly the presence of a long-tailed but monotone decline in the probability of interaction with distance, on disease diffusion. Based on simulations of unrestricted COVID-19 diffusion in 19 US cities, we conclude that heterogeneity in population distribution can have large impacts on local pandemic timing and severity, even when aggregate behavior at larger scales mirrors a classic SIR-like pattern. Impacts observed include severe local outbreaks with long lag time relative to the aggregate infection curve, and the presence of numerous areas whose disease trajectories correlate poorly with those of neighboring areas. A simple catchment model for hospital demand illustrates potential implications for health care utilization, with substantial disparities in the timing and extremity of impacts even without distancing interventions. Likewise, analysis of social exposure to others who are morbid or deceased shows considerable variation in how the epidemic can appear to individuals on the ground, potentially affecting risk assessment and compliance with mitigation measures. These results demonstrate the potential for spatial network structure to generate highly nonuniform diffusion behavior even at the scale of cities, and suggest the importance of incorporating such structure when designing models to inform health care planning, predict community outcomes, or identify potential disparities.

Production of Distinct Fibrillar, Oligomeric, and Other Aggregation States from Network Models of Multibody Interaction.

Protein aggregation can produce a wide range of states, ranging from fibrillar structures and oligomers to unstructured and semistructured gel phases. Recent work has shown that many of these states can be recapitulated by relatively simple, topological models specified in terms of multibody interaction energies, providing a direct connection between aggregate intermolecular forces and aggregation products. Here, we examine a low-dimensional network Hamiltonian model (NHM) based on four basic multibody interactions found in any aggregate system. We characterize the phase behavior of this NHM family, showing that fibrils arise from a balance between elongation-inducing and contact-inhibiting forces. Complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer's disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions. We characterize the phase behavior of this NHM family, demonstrating the range of ordered and disordered aggregation states possible with this set of interactions. As we show, fibrils arise from a balance between elongation-inducing and contact-inhibiting forces, existing in a regime bounded by gel-like and disaggregated phases; complex oligomers (including annular oligomers resembling those thought to be toxic species in Alzheimer's disease) also form distinct phases in this regime, controlled in part by closure-inducing forces. We show that phase structure is largely independent of system size, allowing generalization to macroscopic systems, and provide evidence of a rich structure of minor oligomeric phases that can arise from appropriate conditions.